IEEE Annual Congress on Artificial Intelligence of Things (IEEE AIoT)

14-16 Dec 2026 · Kobe, Japan.

http://www.ieee-aiot.org/2026

Track 4: Security, Trust, Privacy in AI and IoT

Track Chairs:
Wei Yang Bryan Lim, Nanyang Technological University, Singapore, bryan.limwy@ntu.edu.sg
Xuelin Cao, Xidian University, China, caoxuelin@xidian.edu.cn
Zeeshan Kaleem, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, zeeshankaleem@gmail.com

Description

AI is transforming the Internet of Things (IoT), evolving from static pattern recognition to autonomous reasoning, generative creation, and proactive decision-making. By 2026, agentic AI, multi-agent collaboration, on-device foundation models, and post‑quantum threats are redefining intelligent IoT systems. Beyond simple enhancement, AI now enables trustless edge collaboration, adaptive security, and resilient human–cyber–physical interactions.

This new AI-native IoT landscape also introduces novel attack surfaces, including supply chain poisoning, AI-driven identity deception, side-channel leaks, and weakened trust in autonomous multi-agent systems. The growing diversity of devices, complexity of generative AI models, rise of TinyML, and urgent demand for post-quantum cryptography and machine unlearning make securing AIoT systems both critical and challenging. We invite original research from academia and industry addressing algorithms, system designs, implementations, and evaluations. Topics include, but are not limited to:

Track Topics

• Agentic AI Governance and Security for Autonomous IoT Systems
• AI Supply Chain Security and Model Provenance in Intelligent IoT
• Post-Quantum Secure AI Architectures for IoT Networks
• Secure AI Agents and Multi-Agent Collaboration in IoT Environments
• AI-powered Deepfake, Synthetic Media, and Identity Attack Detection for IoT
• TinyML Security and Trustworthy On-Device Learning for Edge IoT
• AI-enhanced Authentication and Access Control for IoT
• AI for Truth Verification and Management in IoT
• AI for Data Privacy in IoT Devices and Services
• Federated Learning in IoT Security
• Edge-Deployed AI Security in IoT
• Incentive Strategies for AI Interaction in IoT
• AI Applications for Smart City IoT Security
• Data Security for AI-powered IoT
• Privacy-enhancing Technologies in Intelligent IoT Systems
• AI-enabled Attacks and Defenses for IoT Devices and Services
• AI for Communication Security of IoT Devices
• Malware Analysis for Intelligent IoT
• Vulnerability Analysis for AI-integrated IoT Devices
• Intelligent Forensics Tools, Techniques, and Procedures for IoT
• Emerging Data Bias Security Issues in Intelligent IoT Systems
• Lightweight Hardware Verification in Intelligent IoT Systems
• Side-Channel Attacks and Defense in Intelligent IoT Systems
• The implications of machine unlearning for security, trust, and privacy in AI and IoT

Paper Submission and Publication

Details of paper submission and publication can be found here.